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Title: International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study.

Authors: Weber, Griffin M; Hong, Chuan; Palmer, Nathan P; Avillach, Paul; Murphy, Shawn N; Gutiérrez-Sacristán, Alba; Xia, Zongqi; Serret-Larmande, Arnaud; Neuraz, Antoine; Omenn, Gilbert S; Visweswaran, Shyam; Klann, Jeffrey G; South, Andrew M; Loh, Ne Hooi Will; Cannataro, Mario; Beaulieu-Jones, Brett K; Bellazzi, Riccardo; Agapito, Giuseppe; Alessiani, Mario; Aronow, Bruce J; Bell, Douglas S; Bellasi, Antonio; Benoit, Vincent; Beraghi, Michele; Boeker, Martin; Booth, John; Bosari, Silvano; Bourgeois, Florence T; Brown, Nicholas W; Bucalo, Mauro; Chiovato, Luca; Chiudinelli, Lorenzo; Dagliati, Arianna; Devkota, Batsal; DuVall, Scott L; Follett, Robert W; Ganslandt, Thomas; García Barrio, Noelia; Gradinger, Tobias; Griffier, Romain; Hanauer, David A; Holmes, John H; Horki, Petar; Huling, Kenneth M; Issitt, Richard W; Jouhet, Vianney; Keller, Mark S; Kraska, Detlef; Liu, Molei; Luo, Yuan; Lynch, Kristine E; Malovini, Alberto; Mandl, Kenneth D; Mao, Chengsheng; Maram, Anupama; Matheny, Michael E; Maulhardt, Thomas; Mazzitelli, Maria; Milano, Marianna; Moore, Jason H; Morris, Jeffrey S; Morris, Michele; Mowery, Danielle L; Naughton, Thomas P; Ngiam, Kee Yuan; Norman, James B; Patel, Lav P; Pedrera Jimenez, Miguel; Ramoni, Rachel B; Schriver, Emily R; Scudeller, Luigia; Sebire, Neil J; Serrano Balazote, Pablo; Spiridou, Anastasia; Tan, Amelia Lm; Tan, Byorn Wl; Tibollo, Valentina; Torti, Carlo; Trecarichi, Enrico M; Vitacca, Michele; Zambelli, Alberto; Zucco, Chiara; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Kohane, Isaac S; Cai, Tianxi; Brat, Gabriel A

Published In medRxiv, (2021 Feb 05)

Abstract: OBJECTIVES: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. DESIGN: Retrospective cohort study. SETTING: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. PARTICIPANTS: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ″ever-severe″ or ″never-severe″ using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. RESULTS: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. CONCLUSIONS: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

PubMed ID: 33564777 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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